Biology subjects

Tegner, J.

Publications and source records attributed to Tegner, J..

At least 19 recordsLinked to original sources

bioRxiv2026

Mapping Disease Transitions from Premalignant, Asymptomatic to Advanced Myeloma through Integrative Epigenomic and Transcriptional Analyses

AbstractMultiple myeloma (MM) evolves from asymptomatic precursor conditions through progressive genetic and epigenetic remodeling, yet the regulatory mechanisms driving the development toward more active stages of the disease remain poorly understood. Here, we integrated bulk-based paired chromatin accessibility and activation, and transcriptomic profiling across disease stages to map regulatory remodeling during myeloma development. We identified a progressive increase in chromatin accessibility; furthermore, this epigenetic reconfiguration is accompanied by a stage-dependent shift from promoter-centered regulation in precursor states toward enhancer-dominated transcriptional control in active MM. Motif enrichment and regulatory network analyses identified both established and previously underappreciated transcription factors (TFs), including members of the IRF, MEF2, and FOX families, associated with disease-stage-specific transcriptional programs. Among these, MEF2D and FOXK2 emerged as candidate regulators of pathways involved in cell survival and chemotaxis. Functional perturbation demonstrated that MEF2D depletion markedly impaired MM cell viability, whereas inhibition of either MEF2D or FOXK2 reduced chemotactic migration. Together, these findings provide a stage-resolved framework of epigenetic and transcriptional remodeling across myeloma development, revealing regulatory programs established in precursor conditions and progressively reinforced during disease evolution, while identifying candidate transcriptional dependencies with potential biological and therapeutic relevance.

cancer biology↗

bioRxiv2026

Establishment of the Saudi Bank of Induced Pluripotent Stem Cells (SBiPSCs): A National Platform for iPSC-Based Research and Therapy

BackgroundThe global landscape of induced pluripotent stem cell (iPSC) resources remains heavily skewed toward European, North American, and East Asian populations, leaving the Middle East and North Africa (MENA) region critically underrepresented. This disparity hinders the application of precision medicine in populations with unique genetic backgrounds, particularly those with high rates of consanguinity and distinctive rare disease profiles. To address this gap, we established the Saudi Bank of Induced Pluripotent Stem Cells (SBiPSCs), at King Abdullah International Medical Research Centre (KAIMRC). The bank comprises two major complementary arms: one dedicated to the derivation and biobanking of iPSCs from individuals with rare and common genetic disorders, and a second focused on Human Leukocyte Antigen (HLA)-based iPSC banking to support the development of immunocompatible cell therapies. MethodsSBiPSCs operates within King Abdulaziz Medical City in Jeddah under the Ministry of National Guard for Health Affairs (MNGHA)s ethical and clinical framework. To establish the repository, we implemented a clinic-guided enrolment strategy in which treating physicians, briefed on the banks objectives, recruited patients with confirmed genetic diagnoses. Peripheral blood samples were collected, processed, and cells were reprogrammed using non-integrating episomal plasmids. All derived lines underwent rigorous quality control in accordance with International Society for Stem Cell Research (ISSCR) standards, including assessment of pluripotency markers, genomic integrity, and trilineage differentiation potential. To demonstrate our iPS characterization workflow and translational utility, iPSCs from a Saudi patient with familial Long QT Syndrome (LQTS) and a healthy sibling were differentiated into functional cardiac organoids. Simultaneously, for the HLA-based banking arm, the Saudi Stem Cell Donor Registry (SSCDR) database was leveraged to identify donors predicted to provide maximal coverage for the Saudi population. ResultsTo date, SBiPSCs has successfully generated 37 iPSC lines derived from 19 Saudi patients and healthy donors. All lines exhibit robust expression of pluripotency markers, maintain normal karyotypes, and demonstrate differentiation capacity. To demonstrate our characterization pipeline and translational utility, iPSCs from an LQTS patient and a healthy sibling were generated, validated, and differentiated into beating cardiac organoids that recapitulated the disease phenotype, with microelectrode array analysis confirming prolonged field potential durations mirroring the clinical QT prolongation. Furthermore, the HLA-based banking arm has expanded to include two homozygous iPSC lines, which together provide immunological compatibility for approximately 9% of the Saudi population. ConclusionsSBiPSCs represents the first centralized iPSC repository in the MENA region. The SBiPSCs is well-positioned to accelerate the translation of stem cell research into scalable, immunocompatible cell therapies and precision medicine applications aligned with national and regional healthcare priorities.

genetics↗

bioRxiv2025

Unify: Learning Cellular Evolution with Universal Multimodal Embeddings

Integrating single-cell RNA-sequencing (scRNA-seq) data across species is hindered by evolutionary divergence, technical batch effects, and the reliance on one-to-one orthologs. We present Unify, a transfer learning methodology that learns universal cell embeddings by defining functionally coherent, multi-modal macrogenes. This is achieved by combining RNA expression with embeddings from protein language models and general-purpose language models. Unify transcends species boundaries, enabling cross-species comparisons beyond strict gene-level homology. Unify corrects batch effects while preserving conserved biological signals across vast evolutionary distances and enables more accurate prediction of perturbation responses across species, such as from mouse to human. Applied to species separated by over 700 million years, Unify reconstructs more accurate multi-species cell-type evolutionary trees and uncovers convergent gene programs. Together, these results establish Unify as a powerful method for comparative single-cell genomics and evolutionary biology.

bioinformatics↗

bioRxiv2025

Charting the single cell transcriptional landscape governing visual imprinting.

Memory-related transcriptional events in brain remain poorly understood. Visual imprinting is a form of learning in which young animals develop preferences through early exposure to specific stimuli. In chicks, visual imprinting memory is stored in the intermediate medial mesopallium (IMM) of the forebrain. To investigate learning-associated molecular changes, we performed single-nucleus RNA sequencing of the left IMM in strongly imprinted chicks and untrained controls. This analysis generated the first classification of cells composing the IMM, identifying as a result over 30 cell clusters with distinct transcriptional differences putatively linked to memory formation, nearly half of them in long non-coding RNAs (lncRNAs). Follow-up analysis on selected genes, confirmed that the gene expression levels of two lncRNAs and protein levels of FOXP2, RORA (transcription factors), LUC7L (splicing factor), and ROBO1 (axon guidance molecule) correlate with memory strength, reflecting either innate learning potential or imprinting experience. Additionally, among the confirmed lncRNAs, the brain- and avian-specific lncRNA ENSGALG00010007489 is enriched in the nuclei of specific glutamatergic clusters and its association with imprinting was further confirmed through quantitative multi-probe in situ hybridization. These findings offer the first single-cell resolution map of transcriptional changes underlying memory formation in the avian brain.

neuroscience↗

bioRxiv2025

Single-cell transcriptional profiling reveals a novel RAB13+ endothelial subpopulation and profibrotic mesenchymal cells in the aged human bone marrow

The bone marrow (BM) microenvironment plays a crucial role in regulating hematopoiesis, yet the molecular changes associated with aging in humans remain poorly understood. Using single-cell RNA sequencing, we uncovered transcriptional shifts in BM endothelial cells (EC) and mesenchymal stem cells (MSC) during aging. Aged sinusoidal EC exhibited a prothrombotic phenotype with compromised mitochondrial and vascular function. Additionally, we identified a novel arterial EC subset, emerging in aged individuals, characterized by RAB13 expression and associated with transcriptional regulatory processes. MSC from aged subjects displayed impaired matrix remodeling and epithelial-mesenchymal transition, driven partly by a subpopulation of THY1+ profibrotic cells absent in younger individuals. Finally, immunofluorescent imaging and spatial transcriptomics confirmed the presence of these aging-associated cells in BM samples from aged individuals. In summary, this work provides a comprehensive view of the transcriptional landscape, cellular interactions, and spatial organization of aged EC and MSC, offering novel insights and potential targets that could be exploited for preventing age-associated changes in humans. TeaserAging reshapes the bone marrow with emergence of RAB13+ endothelial cells and profibrotic stromal cells altering tissue function.

molecular biology↗

bioRxiv2024

GeneSetCluster 2.0: a comprehensive toolset for summarizing and integrating gene-sets analysis

BackgroundGene-Set Analysis (GSA) is commonly used to analyze high-throughput experiments. However, GSA cannot readily disentangle clusters or pathways due to redundancies in upstream knowledge bases, which hinders comprehensive exploration and interpretation of biological findings. To address this challenge, we developed GeneSetCluster, an R package designed to summarize and integrate GSA results. Over time, we and users as well identified limitations in the original version, such as difficulties in managing redundancies across multiple gene-sets, large computational times, and its lack of accessibility for users without programming expertise. ResultsWe present GeneSetCluster 2.0, a comprehensive upgrade that delivers methodological, computational, interpretative, and user-experience enhancements. Methodologically, GeneSetCluster 2.0 introduces a novel approach to address duplicated gene-sets and implements a seriation-based clustering algorithm that reorders results, aiding pattern identification. Computationally, the package is optimized for parallel processing, significantly reducing execution time. GeneSetCluster 2.0 enhances cluster annotations by associating clusters with relevant tissues and biological processes to improve biological interpretation, particularly for human and mouse data. To broaden accessibility, we have developed a user-friendly web application enabling non-programmers to use it. This version also ensures seamless integration between the R package, catering to users with programming expertise, and the web application for broader audiences. We evaluated the updates in a single-cell RNA public dataset. ConclusionGeneSetCluster 2.0 offers substantial improvements over its predecessor. Furthermore, by bridging the gap between bioinformaticians and clinicians in multidisciplinary teams, GeneSetCluster 2.0 facilitates collaborative research. The R package and web application, along with detailed installation and usage guides, are available on GitHub (https://github.com/TranslationalBioinformaticsUnit/GeneSetCluster2.0), and the web application can be accessed at https://translationalbio.shinyapps.io/genesetcluster/.

bioinformatics↗

bioRxiv2024

Unravelling the molecular activation of the reparative cardiac fibroblasts after myocardial infarction

Activated cardiac fibroblasts (Postn+ CFs) are responsible for the healing of the heart tissue after a myocardial infarction (MI). However, so far little is known about the moment that CFs are activated, and the genes involved in this process. This is especially relevant in the context of CF heterogeneity and their role in the response to the damage. In this context, we have described a subpopulation of activated CFs responsible for the healing scar and for preventing the rupture of the ventricle after the damage: the Reparative Cardiac Fibroblasts (RCFs). Our new data indicate that RCFs directly derived from activated CFs, and this transcriptional shift happens in a close window after damage. Interestingly, our results exhibited two different molecular dynamics that would give rise to this activation and, consequently, the appearance of definitive RCFs. Using bulk RNA-Seq, RNAScope and Spatial Transcriptomics, we anatomically localized some of the genes related to both dynamics in the infarcted heart and highlight the potential role of Aspn as a new marker of this transcriptional transition in mice, pigs and patients.

bioinformatics↗

bioRxiv2024

PrePR-CT: Predicting Perturbation Responses in Unseen Cell Types Using Cell-Type-Specific Graphs

Predicting the transcriptional response of chemical perturbations is crucial to understanding gene function and developing drug candidates, promising a streamlined drug development process. Single-cell sequencing has provided an ideal data basis for training machine learning models for this task. Recent advances in deep learning have led to significant improvements in predictions of chemical as well as genetic perturbations at the single cell level. Experiments have shown that different cell types exhibit distinct transcriptional patterns and responses to perturbation. This poses a fundamental problem for predicting transcriptional responses of drugs or cell types outside the training data. Accordingly, existing methods lack cell-type-specific modeling or do not explicitly provide an interpretable mechanism for the gene features. In this study, we introduce a novel approach that employs a network representation of various cell types as an inductive bias, improving prediction performance in scenarios with limited data while acknowledging cellular differences. We applied our framework to four small-scale single-cell perturbation datasets and one large-scale screening experiment, demonstrating that this representation can inherently generalize to previously unseen cell types. Furthermore, our method outperforms the state-of-the-art methods in predicting the post-perturbation response in unobserved cell types.

bioinformatics↗

bioRxiv2024

SpatialDiffusion: Predicting Spatial Transcriptomics with DenoisingDiffusion Probabilistic Models

Spatial Transcriptomics (ST) allows deep characterization of the 2D organization of expression data within tissue slices. The ST technology provides a tissue contextualization of deep single-cell profiles. Recently, numerous computational and machine learning methods have addressed challenges such as data quality, augmentation, annotation, and the development of integrative platforms for data analysis. In contrast, here we ask whether unseen spatial transcriptomics data can be predicted and if we can interpolate novel transcriptomic slices. To this end, we adopt a denoising diffusion probabilistic-based model (DDPM) to demonstrate the learning of generative ST models for several tissues. Furthermore, our generative diffusion model interpolates (predicts) unseen slices located "between" the collected finite number of ST slices. This methodology set the stage for learning predictive deep 3D models of tissues from a finite number of spatial transcriptomics slices, thus harboring the advent of AI-augmented spatial transcriptomics.

bioinformatics↗

bioRxiv2024

Transcriptional Characterization of the Stromal and Endothelial Bone Marrow Microenvironment during Progression from MGUS to Multiple Myeloma

The role of the non-immune bone marrow microenvironment (BME) in the transition from monoclonal gammopathy of undetermined significance (MGUS) into clinically active multiple myeloma (MM) remains incompletely defined. To address this, we transcriptionally profiled endothelial cells (EC), mesenchymal stem cells (MSC) and MM cells at single-cell resolution from two genetically engineered mouse models (BIc{gamma}1 and MIc{gamma}1) that recapitulate MGUS to MM progression. Our analysis revealed distinct transcriptional trajectories in EC and MSC, uncovering stage-specific BME-PC interactions shaping disease progression. EC acquired a stress phenotype during MGUS transitioning to angiogenesis in MM, while MSC exhibited early impaired differentiation capacity during MGUS that persisted in MM. Notably, an interferon (IFN)-associated MM signature was detected in EC and MSC from the BIc{gamma}1 model but was absent in the more aggressive MIc{gamma}1 model. Treatment with bortezomib, lenalidomide, and dexamethasone remodeled the BME by suppressing MM-IFN signaling, promoting an adaptive response in EC, and restoring osteogenic potential in MSC-- shifting the niche toward a less tumor-permissive state. Importantly, the MM-IFN signature was validated in patients across the MGUS-to-MM spectrum, supporting the translational relevance of our findings. Together, these data define novel dynamic and targetable alterations in the non-immune BME during myeloma progression. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=97 SRC="FIGDIR/small/589777v5_ufig1.gif" ALT="Figure 1"> View larger version (28K): org.highwire.dtl.DTLVardef@1a0ef62org.highwire.dtl.DTLVardef@c44014org.highwire.dtl.DTLVardef@4b1df6org.highwire.dtl.DTLVardef@19c29a2_HPS_FORMAT_FIGEXP M_FIG C_FIG

cancer biology↗

bioRxiv2023

Joint Learning of Node Semantics and Graph Topology using a Transformer in the sparse network regime

The human interactome is a valuable tool for unraveling disease mechanisms, advancing precision medicine, facilitating drug discovery, and identifying biomarkers. Yet, current interactomes are incomplete, in part due to limited experimental coverage. Therefore, augmenting the human interactome by predicting missing links in the Protein-Protein interaction network (PPI), is a core challenge for precision medicine. This study proposes an end-to-end trainable transformer-based neural network for enhanced aggregation of Gene Ontology (GO) terms features. We augment the models predictive capabilities by incorporating semantic anc2vec features, complementing the structural node2vec embeddings specifically designed for sparse PPIs. As a result, by integrating semantic and graph features, we demonstrate superior performance in link prediction.

bioinformatics↗

bioRxiv2023

Representational Learning from Healthy Multi-Tissue Human RNA-seq Data such that Latent Space Arithmetics Extracts Disease Modules

1Developing computational analyses of transcriptomic data has dramatically improved our understanding of complex multifactorial diseases. However, such approaches are limited to small sample sets of disease-affected material, thus being sensitive to statistical biases and noise. Here, we ask if a variational autoencoder (VAE) trained on large groups of healthy, human RNA-seq data of multiple tissues can capture the fundamental healthy gene regulation system such that the learned representation generalizes to account for unseen disease changes. To this end, we trained a multi-scale representation to encode cellular processes ranging from cell types to genegene interactions. Importantly, we found that the learned healthy representations could predict unseen gene expression changes from 25 independent disease datasets. We extracted and decoded disease-specific signals from the VAE latent space to dissect this finding. Interestingly, the gene modules corresponding to this signal contained more disease-specific genes than the respective differential expression analysis in 20 of 25 cases. Finally, we matched genes related to the disease signals to known drug targets. We could extract sets of known and potential pharmaceutical candidates from this analysis and demonstrate the utility in three use cases. In summary, our study showcases how data-driven representation learning using a VAE as a foundational model allows an arithmetic deconstruction of the latent space such that biological insights enable the dissection of disease mechanisms and drug targets. Our model is available at https://github.com/ddeweerd/VAE_Transcriptomics/.

systems biology↗

bioRxiv2023

HLA-Based Banking of Human Induced Pluripotent Stem Cells in Saudi Arabia

Human iPSCs derivation and use in clinical studies are transforming medicine. Yet, there is a high cost and long waiting time for autologous iPS-based cellular therapy, and the genetic engineering of hypo-immunogenic iPS cell lines is hampered with numerous hurdles. Therefore, it is increasingly interesting to create cell stocks based on HLA haplotype distribution in a given population. In this study, we assessed the potential of HLA-based iPS banking for the Saudi population. First, we analyzed the HLA database of the Saudi Stem Cell Donor Registry (SSCDR), which contains high-resolution HLA genotype data of 64,315 registered Saudi donors at the time of analysis. We found that only 13 iPS lines would be required to cover 30% of the Saudi population, 39 iPS lines would offer 50% coverage and 596 for more than 90% coverage. Next, As a proof-of-concept, we launched the first HLA-based banking of iPSCs in Saudi Arabia. Using clinically relevant methods, we generated the first iPSC line from a homozygous donor for the most common HLA haplotype in Saudi. The two generated clones expressed pluripotency markers, could be differentiated into all three germ layers, beating cardiomyocytes and neuronal progenitors. To ensure that our reprogramming method generates genetically stable iPSCs, we assessed the mutational burden in the generated clones and the original blood sample from which the iPSCs were derived using whole-genome sequencing. All detected variants were found in the original donor sample and were classified as benign according to current guidelines of the American College of Medical Genetics and Genomics (ACMG). This study sets a road map for introducing iPS-based cell therapy in the Kingdom of Saudi Arabia.

genetics↗

bioRxiv2023

LEP-AD: Language Embedding of Proteins and Attention to Drugs predicts drug target interactions

AO_SCPLOWBSTRACTC_SCPLOWPredicting drug-target interactions is a tremendous challenge for drug development and lead optimization. Recent advances include training algorithms to learn drug-target interactions from data and molecular simulations. Here we utilize Evolutionary Scale Modeling (ESM-2) models to establish a Transformer protein language model for drug-target interaction predictions. Our architecture, LEP-AD, combines pre-trained ESM-2 and Transformer-GCN models predicting binding affinity values. We report new best-in-class state-of-the-art results compared to competing methods such as SimBoost, DeepCPI, Attention-DTA, GraphDTA, and more using multiple datasets, including Davis, KIBA, DTC, Metz, ToxCast, and STITCH. Finally, we find that a pre-trained model with embedding of proteins (the LED-AD) outperforms a model using an explicit alpha-fold 3D representation of proteins (e.g., LEP-AD supervised by Alphafold). The LEP-AD model scales favorably in performance with the size of training data. Code available at https://github.com/adaga06/LEP-AD

bioinformatics↗

bioRxiv2022

scAEGAN: Unification of Single-Cell Genomics Data by Adversarial Learning of Latent Space Correspondences

Recent progress in Single-Cell Genomics have produced different library protocols and techniques for profiling of one or more data modalities in individual cells. Machine learning methods have separately addressed specific integration challenges (libraries, samples, paired-unpaired data modalities). We formulate an unifying data-driven methodology addressing all these challenges. To this end, we design a hybrid architecture using an autoencoder (AE) network together with adversarial learning by a cycleGAN (cGAN) network, jointly referred to as scAEGAN. The AE learns a low-dimensional embedding of each condition, whereas the cGAN learns a non-linear mapping between the AE representations. The core insight is that the AE respects each samples uniqueness, whereas the cGAN exploits the distributional data similarity in the latent space. We evaluate scAEGAN using simulated data and real datasets of a single-modality (scRNA-seq), different library preparations (Fluidigm C1, CelSeq, CelSeq2, SmartSeq), and several data modalities such as paired scRNA-seq and scATAC-seq. We find that scAEGAN outperforms Seurat3 in library integration, is more robust against data sparsity, and beats Seurat 4 in integrating paired data from the same cell. Furthermore, in predicting one data modality from another, scAEGAN outperforms Babel. We conclude scAEGAN surpasses current state-of-the-art methods across several seemingly different integration challenges.

bioinformatics↗

bioRxiv2021

Deep deconvolution of the hematopoietic stem cell regulatory microenvironment reveals a high degree of specialization and conservation between mouse and human.

Understanding the regulation of normal and malignant human hematopoiesis requires comprehensive cell atlas of the hematopoietic stem cell (HSC) regulatory microenvironment. Here, we develop a tailored bioinformatic pipeline to integrate public and proprietary single-cell RNA sequencing (scRNA-seq) datasets. As a result, we robustly identify for the first time 14 intermediate cell states and 11 stages of differentiation in the endothelial and mesenchymal BM compartments, respectively. Our data provide the most comprehensive description to date of the murine HSC-regulatory microenvironment and suggests a higher level of specialization of the cellular circuits than previously anticipated. Furthermore, this deep characterization allows to infer conserved features in human, suggesting that the layers of microenvironmental regulation of hematopoiesis may also be shared between species. Our resource and methodology are a steppingstone towards a comprehensive cell atlas of the BM microenvironment.

immunology↗

bioRxiv2021

White-box Deep Neural Network Prediction of Genome-Wide Transcriptome Signatures

Prediction algorithms for protein or gene structures, including transcription factor binding from sequence information, have been transformative in understanding gene regulation. Here we ask whether human transcriptomic profiles can be predicted solely from the expression of transcription factors (TFs). To this end, we explore whether a neural network (NN) could predict the transcriptome from TFs. Using at least one hidden layer, we find that the expression of 1,600 TFs can explain >95% of variance in 25,000 genes. Using the light-up technique to inspect the trained NN, we find an overrepresentation of known TF-gene regulations. Furthermore, the learned prediction network has a hierarchical organization. A smaller set of around 125 core TFs could explain close to 80% of the variance. Interestingly, reducing the number of TFs below 500 induces a rapid decline in prediction performance. Next, we evaluated the prediction model using transcriptional data from 22 human diseases. The TFs were sufficient to predict the target genes dysregulation (rho=0.61, P < 10-216). By inspecting the model, key causative TFs could be extracted for subsequent validation using disease-associated genetic variants. In conclusion, we demonstrate the construction of an interpretable neural network predictor. Analysis of the predictors revealed key TFs that were inducing transcriptional changes during disease.

systems biology↗

bioRxiv2021

Machine Translation between paired Single Cell Multi Omics Data

BackgroundSingle-cell multi-omics technologies allow the profiling of different data modalities from the same cell. However, while isolated modalities only capture one view of the total information of a biological cell, an integrative analysis capturing the different modalities is challenging. In response, bioinformatics and machine learning methodologies have been developed for multi-omics single-cell analysis. Nevertheless, it is unclear if current tools can address the dual aspect of modality integration and prediction across modalities without requiring extensive parameter finetuning. ResultsWe designed LIBRA, a Neural Network based framework, to learn a translation between paired multi-omics profiles such that a shared latent space is constructed. LIBRA is a state-of-the-art tool when evaluating the ability to increase cell-type (clustering) resolution in the latent space. When assessing the predictive power across data modalities, LIBRA outperforms existing tools. Finally, considering the importance of hyperparameters, we implemented an adaptative-tuning strategy, labelled aLIBRA, in the LIBRA package. As expected, adaptive parameter optimization significantly boosts the performance of learning predictive models from paired datasets. Additionally, aLIBRA provides parameter combinations balancing the integrative and predictive tasks. ConclusionsLIBRA is a versatile tool, uniquely targeting both integration and prediction tasks of Single-cell multi-omics data. LIBRA is a data-driven robust platform that includes an adaptive learning scheme. Furthermore, LIBRA is freely available as R and Python libraries (https://github.com/TranslationalBioinformaticsUnit/LIBRA).

bioinformatics↗